In wireless communication systems, receivers inherently receive signals that include noise, such as thermal noise and interference. Measuring or estimating levels of noise is an important aspect in wireless communication systems because it enables the original data to be separated from the noise. Noise estimation can be used for tuning the receiver to the characteristics of the noise and for determining the modulation and coding scheme used for the signal. Hence, there are a number of methods that are used to estimate the characteristics of noise within such systems.
One particular method estimates the signal-to-noise ratio (SNR) from a signal at an input to a Forward Error Correction (FEC) decoder. The signal is assumed to be equalized before demodulation, which includes processes such as constellation slicing and soft metric calculation. Although the signal power is known exactly, the problem is that the signal is modulated by unknown data and it is therefore difficult to separate the noise from the data.
The method of hard slicing is commonly used to estimate Channel to Interference & Noise Ratio (CINR) on modulated data symbols of various modulation schemes. Hard slicing decodes the data from the signal and calculates the mean square distance between received and decoded points. However, this method has a bias of 2-7 dB, which is generated by decoding errors (i.e. uncoded Bit Error Rate (BER)) and depends on the constellation and on the noise/interference distribution. Since most communication systems are coded, the uncoded BER is high at FEC working points. This is further exacerbated when implemented with fading channels, which generates a larger bias in FEC working points.
Another method commonly used when accurate CINR estimation is required includes decoding the data using a FEC decoder, re-encoding the data, subtracting the received signal from the re-encoded signal and calculating the mean square error to provide a CINR estimation. However, this method is complex to implement, requires storing of the received signal and encoding of the signal in parallel with decoding. Furthermore, this method fails below the code working point.
Other, less common algorithms have been suggested to solve one or more of the aforementioned problems, but many assume a Gaussian noise distribution and therefore are less suitable for interference limited systems. Also, some of these algorithms use batch processing of the data, which complicates the implementation.
Skilled addressees will appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the relative dimensions of some of the elements in the figures may be distorted to help improve understanding of embodiments of the present invention.